Overview

Dataset statistics

Number of variables18
Number of observations11010940
Missing cells14099601
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 GiB
Average record size in memory144.0 B

Variable types

Numeric7
Categorical11

Alerts

start_time has a high cardinality: 9544 distinct values High cardinality
blueprint has a high cardinality: 439 distinct values High cardinality
login has a high cardinality: 1660 distinct values High cardinality
position_x is highly correlated with map_width and 1 other fieldsHigh correlation
position_y is highly correlated with map_width and 1 other fieldsHigh correlation
map_width is highly correlated with position_x and 2 other fieldsHigh correlation
map_height is highly correlated with position_x and 2 other fieldsHigh correlation
position_x is highly correlated with map_width and 1 other fieldsHigh correlation
position_y is highly correlated with map_width and 1 other fieldsHigh correlation
map_width is highly correlated with position_x and 2 other fieldsHigh correlation
map_height is highly correlated with position_x and 2 other fieldsHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
map_name is highly correlated with map_height and 2 other fieldsHigh correlation
map_height is highly correlated with map_name and 2 other fieldsHigh correlation
map_width is highly correlated with map_name and 2 other fieldsHigh correlation
map_thumbnail is highly correlated with map_name and 2 other fieldsHigh correlation
position_x is highly correlated with position_y and 4 other fieldsHigh correlation
position_y is highly correlated with position_x and 4 other fieldsHigh correlation
faf_player_id is highly correlated with ratingHigh correlation
rating is highly correlated with faf_player_id and 2 other fieldsHigh correlation
map_name is highly correlated with position_x and 5 other fieldsHigh correlation
map_thumbnail is highly correlated with position_x and 5 other fieldsHigh correlation
map_width is highly correlated with position_x and 4 other fieldsHigh correlation
map_height is highly correlated with position_x and 4 other fieldsHigh correlation
blueprint has 7843923 (71.2%) missing values Missing
position_x has 3127839 (28.4%) missing values Missing
position_y has 3127839 (28.4%) missing values Missing
units_number has 287757 (2.6%) zeros Zeros
rating has 167306 (1.5%) zeros Zeros

Reproduction

Analysis started2022-07-28 02:47:56.814525
Analysis finished2022-07-28 03:00:08.022576
Duration12 minutes and 11.21 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

Distinct9760
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16144023.3
Minimum16026248
Maximum16260639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 MiB
2022-07-27T23:00:08.076826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16026248
5-th percentile16039042
Q116085831
median16142786
Q316204879
95-th percentile16249707
Maximum16260639
Range234391
Interquartile range (IQR)119048

Descriptive statistics

Standard deviation67898.56457
Coefficient of variation (CV)0.004205801943
Kurtosis-1.209681161
Mean16144023.3
Median Absolute Deviation (MAD)59113
Skewness0.01914461842
Sum1.777608719 × 1014
Variance4610215070
MonotonicityNot monotonic
2022-07-27T23:00:08.143174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160972678487
 
0.1%
160528828439
 
0.1%
162545527648
 
0.1%
162130597635
 
0.1%
162560427315
 
0.1%
162152847222
 
0.1%
160827906981
 
0.1%
161106926791
 
0.1%
161039686368
 
0.1%
161160216308
 
0.1%
Other values (9750)10937746
99.3%
ValueCountFrequency (%)
16026248428
 
< 0.1%
1602624920
 
< 0.1%
160262751262
< 0.1%
160262811643
< 0.1%
160262942159
< 0.1%
16026360500
 
< 0.1%
16026367809
 
< 0.1%
160264062493
< 0.1%
160264301009
< 0.1%
1602643144
 
< 0.1%
ValueCountFrequency (%)
16260639956
< 0.1%
16260604802
 
< 0.1%
162605931025
< 0.1%
16260580682
 
< 0.1%
162605201724
< 0.1%
16260519522
 
< 0.1%
16260434665
 
< 0.1%
162603721409
< 0.1%
16260346625
 
< 0.1%
162603212033
< 0.1%

start_time
Categorical

HIGH CARDINALITY

Distinct9544
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
2022-01-09 23:41:39 UTC
 
8487
2022-01-04 12:11:23 UTC
 
8439
2022-01-31 03:29:21 UTC
 
7648
2022-01-25 22:00:30 UTC
 
7635
2022-01-31 11:32:15 UTC
 
7315
Other values (9539)
10971416 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters253251620
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique228 ?
Unique (%)< 0.1%

Sample

1st row2022-01-14 20:00:38 UTC
2nd row2022-01-21 17:44:31 UTC
3rd row2022-01-31 11:47:12 UTC
4th row2022-01-14 20:39:32 UTC
5th row2022-01-01 17:56:41 UTC

Common Values

ValueCountFrequency (%)
2022-01-09 23:41:39 UTC8487
 
0.1%
2022-01-04 12:11:23 UTC8439
 
0.1%
2022-01-31 03:29:21 UTC7648
 
0.1%
2022-01-25 22:00:30 UTC7635
 
0.1%
2022-01-31 11:32:15 UTC7315
 
0.1%
2022-01-26 08:48:29 UTC7222
 
0.1%
2022-01-08 12:17:16 UTC6981
 
0.1%
2022-01-12 01:37:39 UTC6791
 
0.1%
2022-01-30 05:50:11 UTC6626
 
0.1%
2022-01-11 01:05:40 UTC6368
 
0.1%
Other values (9534)10937428
99.3%

Length

2022-07-27T23:00:08.199167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc11010940
33.3%
2022-01-29478042
 
1.4%
2022-01-08453272
 
1.4%
2022-01-16446787
 
1.4%
2022-01-30440144
 
1.3%
2022-01-23424881
 
1.3%
2022-01-09417363
 
1.3%
2022-01-15400498
 
1.2%
2022-01-03398243
 
1.2%
2022-01-02383163
 
1.2%
Other values (8385)18179487
55.0%

Most occurring characters

ValueCountFrequency (%)
248510811
19.2%
035071564
13.8%
128562054
11.3%
-22021880
8.7%
22021880
8.7%
:22021880
8.7%
311020829
 
4.4%
U11010940
 
4.3%
T11010940
 
4.3%
C11010940
 
4.3%
Other values (6)30987902
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number154153160
60.9%
Uppercase Letter33032820
 
13.0%
Dash Punctuation22021880
 
8.7%
Space Separator22021880
 
8.7%
Other Punctuation22021880
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
248510811
31.5%
035071564
22.8%
128562054
18.5%
311020829
 
7.1%
47217885
 
4.7%
56638927
 
4.3%
94546135
 
2.9%
84415815
 
2.9%
64093927
 
2.7%
74075213
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
U11010940
33.3%
T11010940
33.3%
C11010940
33.3%
Dash Punctuation
ValueCountFrequency (%)
-22021880
100.0%
Space Separator
ValueCountFrequency (%)
22021880
100.0%
Other Punctuation
ValueCountFrequency (%)
:22021880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common220218800
87.0%
Latin33032820
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
248510811
22.0%
035071564
15.9%
128562054
13.0%
-22021880
10.0%
22021880
10.0%
:22021880
10.0%
311020829
 
5.0%
47217885
 
3.3%
56638927
 
3.0%
94546135
 
2.1%
Other values (3)12584955
 
5.7%
Latin
ValueCountFrequency (%)
U11010940
33.3%
T11010940
33.3%
C11010940
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII253251620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
248510811
19.2%
035071564
13.8%
128562054
11.3%
-22021880
8.7%
22021880
8.7%
:22021880
8.7%
311020829
 
4.4%
U11010940
 
4.3%
T11010940
 
4.3%
C11010940
 
4.3%
Other values (6)30987902
12.2%

offset_ms
Real number (ℝ≥0)

Distinct63426
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778233.6821
Minimum0
Maximum10604000
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size84.0 MiB
2022-07-27T23:00:08.254286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72400
Q1301800
median589700
Q31021500
95-th percentile2134200
Maximum10604000
Range10604000
Interquartile range (IQR)719700

Descriptive statistics

Standard deviation718418.5647
Coefficient of variation (CV)0.9231399016
Kurtosis11.77352656
Mean778233.6821
Median Absolute Deviation (MAD)334800
Skewness2.511837487
Sum8.569084379 × 1012
Variance5.16125234 × 1011
MonotonicityNot monotonic
2022-07-27T23:00:08.316210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
716001151
 
< 0.1%
725001144
 
< 0.1%
723001144
 
< 0.1%
724001143
 
< 0.1%
860001141
 
< 0.1%
710001141
 
< 0.1%
721001133
 
< 0.1%
878001130
 
< 0.1%
717001117
 
< 0.1%
731001115
 
< 0.1%
Other values (63416)10999581
99.9%
ValueCountFrequency (%)
08
 
< 0.1%
15001
 
< 0.1%
17002
 
< 0.1%
18003
 
< 0.1%
19006
 
< 0.1%
2000113
< 0.1%
210012
 
< 0.1%
220016
 
< 0.1%
230018
 
< 0.1%
240013
 
< 0.1%
ValueCountFrequency (%)
106040001
< 0.1%
106010001
< 0.1%
106005001
< 0.1%
106001001
< 0.1%
105994001
< 0.1%
105993001
< 0.1%
105989001
< 0.1%
105981001
< 0.1%
105940001
< 0.1%
105919001
< 0.1%

player
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
1
5529239 
0
5481701 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11010940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

Length

2022-07-27T23:00:08.370077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:00:08.707979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

Most occurring characters

ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11010940
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common11010940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11010940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15529239
50.2%
05481701
49.8%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
issue
10640533 
factory_issue
 
370407

Length

Max length13
Median length5
Mean length5.269119258
Min length5

Characters and Unicode

Total characters58017956
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowissue
2nd rowissue
3rd rowissue
4th rowissue
5th rowissue

Common Values

ValueCountFrequency (%)
issue10640533
96.6%
factory_issue370407
 
3.4%

Length

2022-07-27T23:00:08.749574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:00:08.799737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
issue10640533
96.6%
factory_issue370407
 
3.4%

Most occurring characters

ValueCountFrequency (%)
s22021880
38.0%
i11010940
19.0%
u11010940
19.0%
e11010940
19.0%
f370407
 
0.6%
a370407
 
0.6%
c370407
 
0.6%
t370407
 
0.6%
o370407
 
0.6%
r370407
 
0.6%
Other values (2)740814
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter57647549
99.4%
Connector Punctuation370407
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s22021880
38.2%
i11010940
19.1%
u11010940
19.1%
e11010940
19.1%
f370407
 
0.6%
a370407
 
0.6%
c370407
 
0.6%
t370407
 
0.6%
o370407
 
0.6%
r370407
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_370407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57647549
99.4%
Common370407
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s22021880
38.2%
i11010940
19.1%
u11010940
19.1%
e11010940
19.1%
f370407
 
0.6%
a370407
 
0.6%
c370407
 
0.6%
t370407
 
0.6%
o370407
 
0.6%
r370407
 
0.6%
Common
ValueCountFrequency (%)
_370407
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII58017956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s22021880
38.0%
i11010940
19.0%
u11010940
19.0%
e11010940
19.0%
f370407
 
0.6%
a370407
 
0.6%
c370407
 
0.6%
t370407
 
0.6%
o370407
 
0.6%
r370407
 
0.6%
Other values (2)740814
 
1.3%

units_number
Real number (ℝ≥0)

ZEROS

Distinct478
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.403310707
Minimum0
Maximum1023
Zeros287757
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size84.0 MiB
2022-07-27T23:00:08.846429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q35
95-th percentile30
Maximum1023
Range1023
Interquartile range (IQR)4

Descriptive statistics

Standard deviation14.50985953
Coefficient of variation (CV)2.265993358
Kurtosis112.9688139
Mean6.403310707
Median Absolute Deviation (MAD)0
Skewness7.182731273
Sum70506470
Variance210.5360236
MonotonicityNot monotonic
2022-07-27T23:00:08.904741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16156664
55.9%
2751997
 
6.8%
3496325
 
4.5%
4375531
 
3.4%
5304250
 
2.8%
0287757
 
2.6%
6249960
 
2.3%
7197167
 
1.8%
8172636
 
1.6%
9151501
 
1.4%
Other values (468)1867152
 
17.0%
ValueCountFrequency (%)
0287757
 
2.6%
16156664
55.9%
2751997
 
6.8%
3496325
 
4.5%
4375531
 
3.4%
5304250
 
2.8%
6249960
 
2.3%
7197167
 
1.8%
8172636
 
1.6%
9151501
 
1.4%
ValueCountFrequency (%)
10231
 
< 0.1%
9961
 
< 0.1%
7753
< 0.1%
7711
 
< 0.1%
7571
 
< 0.1%
7501
 
< 0.1%
7441
 
< 0.1%
7292
< 0.1%
7262
< 0.1%
7182
< 0.1%

blueprint
Categorical

HIGH CARDINALITY
MISSING

Distinct439
Distinct (%)< 0.1%
Missing7843923
Missing (%)71.2%
Memory size84.0 MiB
ueb1103
247084 
urb1103
 
194437
uel0201
 
102051
xsb1103
 
99743
url0107
 
96014
Other values (434)
2427688 

Length

Max length22
Median length7
Mean length7.002881576
Min length7

Characters and Unicode

Total characters22178245
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowueb1103
2nd rowzeb9501
3rd rowuab1103
4th rowurb0101
5th rowuab1103

Common Values

ValueCountFrequency (%)
ueb1103247084
 
2.2%
urb1103194437
 
1.8%
uel0201102051
 
0.9%
xsb110399743
 
0.9%
url010796014
 
0.9%
uel010590410
 
0.8%
ueb010189797
 
0.8%
uab110388685
 
0.8%
ueb110174725
 
0.7%
url010568339
 
0.6%
Other values (429)2015732
 
18.3%
(Missing)7843923
71.2%

Length

2022-07-27T23:00:08.960967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ueb1103247084
 
7.8%
urb1103194437
 
6.1%
uel0201102051
 
3.2%
xsb110399743
 
3.1%
url010796014
 
3.0%
uel010590410
 
2.9%
ueb010189797
 
2.8%
uab110388685
 
2.8%
ueb110174725
 
2.4%
url010568339
 
2.2%
Other values (429)2015732
63.6%

Most occurring characters

ValueCountFrequency (%)
04786196
21.6%
14443340
20.0%
u2581461
11.6%
b1917162
8.6%
21357505
 
6.1%
e1263526
 
5.7%
31161854
 
5.2%
r974974
 
4.4%
l959152
 
4.3%
a676335
 
3.0%
Other values (23)2056740
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12664416
57.1%
Lowercase Letter9512017
42.9%
Connector Punctuation1812
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u2581461
27.1%
b1917162
20.2%
e1263526
13.3%
r974974
 
10.2%
l959152
 
10.1%
a676335
 
7.1%
s543616
 
5.7%
x525337
 
5.5%
z30271
 
0.3%
d30031
 
0.3%
Other values (12)10152
 
0.1%
Decimal Number
ValueCountFrequency (%)
04786196
37.8%
14443340
35.1%
21357505
 
10.7%
31161854
 
9.2%
5378076
 
3.0%
4252384
 
2.0%
7109992
 
0.9%
680636
 
0.6%
947301
 
0.4%
847132
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_1812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12666228
57.1%
Latin9512017
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
u2581461
27.1%
b1917162
20.2%
e1263526
13.3%
r974974
 
10.2%
l959152
 
10.1%
a676335
 
7.1%
s543616
 
5.7%
x525337
 
5.5%
z30271
 
0.3%
d30031
 
0.3%
Other values (12)10152
 
0.1%
Common
ValueCountFrequency (%)
04786196
37.8%
14443340
35.1%
21357505
 
10.7%
31161854
 
9.2%
5378076
 
3.0%
4252384
 
2.0%
7109992
 
0.9%
680636
 
0.6%
947301
 
0.4%
847132
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII22178245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04786196
21.6%
14443340
20.0%
u2581461
11.6%
b1917162
8.6%
21357505
 
6.1%
e1263526
 
5.7%
31161854
 
5.2%
r974974
 
4.4%
l959152
 
4.3%
a676335
 
3.0%
Other values (23)2056740
9.3%

position_x
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5142308
Distinct (%)65.2%
Missing3127839
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean243.2068727
Minimum-0.9994506836
Maximum1023.636841
Zeros4
Zeros (%)< 0.1%
Negative265
Negative (%)< 0.1%
Memory size84.0 MiB
2022-07-27T23:00:09.056526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9994506836
5-th percentile34.24407196
Q1109.5
median206.5
Q3347.1822815
95-th percentile524.2194824
Maximum1023.636841
Range1024.636292
Interquartile range (IQR)237.6822815

Descriptive statistics

Standard deviation173.9242276
Coefficient of variation (CV)0.7151287528
Kurtosis1.488285957
Mean243.2068727
Median Absolute Deviation (MAD)112.869194
Skewness1.137607706
Sum1917224342
Variance30249.63694
MonotonicityNot monotonic
2022-07-27T23:00:09.114580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.516968
 
0.2%
40.514023
 
0.1%
473.512737
 
0.1%
51.511269
 
0.1%
41.511155
 
0.1%
49.510609
 
0.1%
35.510355
 
0.1%
168.59948
 
0.1%
50.59663
 
0.1%
206.59551
 
0.1%
Other values (5142298)7766823
70.5%
(Missing)3127839
28.4%
ValueCountFrequency (%)
-0.99945068362
< 0.1%
-0.9989166261
< 0.1%
-0.99671936041
< 0.1%
-0.99548339841
< 0.1%
-0.99458312991
< 0.1%
-0.99424743651
< 0.1%
-0.99263763431
< 0.1%
-0.99256134031
< 0.1%
-0.9918975831
< 0.1%
-0.99033355711
< 0.1%
ValueCountFrequency (%)
1023.6368411
< 0.1%
1023.5186771
< 0.1%
1023.4957281
< 0.1%
1023.3983151
< 0.1%
1023.1842041
< 0.1%
1023.024781
< 0.1%
1022.9073491
< 0.1%
1022.8227541
< 0.1%
1022.7465821
< 0.1%
1022.7368161
< 0.1%

position_y
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5007556
Distinct (%)63.5%
Missing3127839
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean243.6739392
Minimum-0.9995269775
Maximum1023.869507
Zeros4
Zeros (%)< 0.1%
Negative112
Negative (%)< 0.1%
Memory size84.0 MiB
2022-07-27T23:00:09.201812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9995269775
5-th percentile34.16803741
Q1107.0003128
median207.829895
Q3337.9939575
95-th percentile544.1104126
Maximum1023.869507
Range1024.869034
Interquartile range (IQR)230.9936447

Descriptive statistics

Standard deviation177.3032438
Coefficient of variation (CV)0.7276249745
Kurtosis1.861659264
Mean243.6739392
Median Absolute Deviation (MAD)113.7306061
Skewness1.246542312
Sum1920906274
Variance31436.44027
MonotonicityNot monotonic
2022-07-27T23:00:09.261956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.513956
 
0.1%
205.513721
 
0.1%
47.512771
 
0.1%
48.512593
 
0.1%
226.511998
 
0.1%
40.511797
 
0.1%
195.511045
 
0.1%
180.510896
 
0.1%
38.510874
 
0.1%
464.510766
 
0.1%
Other values (5007546)7762684
70.5%
(Missing)3127839
28.4%
ValueCountFrequency (%)
-0.99952697751
< 0.1%
-0.984878541
< 0.1%
-0.97766876221
< 0.1%
-0.96395111081
< 0.1%
-0.96225738531
< 0.1%
-0.92573547361
< 0.1%
-0.92111206051
< 0.1%
-0.91566467291
< 0.1%
-0.91291809081
< 0.1%
-0.90819549561
< 0.1%
ValueCountFrequency (%)
1023.8695071
< 0.1%
1023.5632321
< 0.1%
1023.5246581
< 0.1%
1023.0013431
< 0.1%
1022.8610231
< 0.1%
1022.8593751
< 0.1%
1022.8583981
< 0.1%
1022.8483891
< 0.1%
1022.6743161
< 0.1%
1022.673951
< 0.1%

command_name
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
Move
5434086 
BuildMobile
1466267 
BuildFactory
1243056 
Reclaim
708243 
Attack
694069 
Other values (26)
1465219 

Length

Max length28
Median length25
Mean length6.694862655
Min length4

Characters and Unicode

Total characters73716731
Distinct characters37
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowStop
2nd rowStop
3rd rowFormMove
4th rowStop
5th rowStop

Common Values

ValueCountFrequency (%)
Move5434086
49.4%
BuildMobile1466267
 
13.3%
BuildFactory1243056
 
11.3%
Reclaim708243
 
6.4%
Attack694069
 
6.3%
Guard458558
 
4.2%
AggressiveMove314470
 
2.9%
Patrol233654
 
2.1%
Upgrade162758
 
1.5%
Repair74497
 
0.7%
Other values (21)221282
 
2.0%

Length

2022-07-27T23:00:09.317202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
move5434086
49.4%
buildmobile1466267
 
13.3%
buildfactory1243056
 
11.3%
reclaim708243
 
6.4%
attack694069
 
6.3%
guard458558
 
4.2%
aggressivemove314470
 
2.9%
patrol233654
 
2.1%
upgrade162758
 
1.5%
repair74497
 
0.7%
Other values (21)221282
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o8899044
12.1%
e8891127
12.1%
M7248407
 
9.8%
v6117773
 
8.3%
i5407627
 
7.3%
l5151305
 
7.0%
a3716039
 
5.0%
d3376345
 
4.6%
u3181654
 
4.3%
t3083295
 
4.2%
Other values (27)18644115
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59525386
80.7%
Uppercase Letter14191345
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o8899044
14.9%
e8891127
14.9%
v6117773
10.3%
i5407627
9.1%
l5151305
8.7%
a3716039
6.2%
d3376345
 
5.7%
u3181654
 
5.3%
t3083295
 
5.2%
c2741364
 
4.6%
Other values (11)8959813
15.1%
Uppercase Letter
ValueCountFrequency (%)
M7248407
51.1%
B2709323
 
19.1%
F1279826
 
9.0%
A1015995
 
7.2%
R784474
 
5.5%
G458558
 
3.2%
P233654
 
1.6%
U231201
 
1.6%
S108932
 
0.8%
T56579
 
0.4%
Other values (6)64396
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin73716731
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o8899044
12.1%
e8891127
12.1%
M7248407
 
9.8%
v6117773
 
8.3%
i5407627
 
7.3%
l5151305
 
7.0%
a3716039
 
5.0%
d3376345
 
4.6%
u3181654
 
4.3%
t3083295
 
4.2%
Other values (27)18644115
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII73716731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o8899044
12.1%
e8891127
12.1%
M7248407
 
9.8%
v6117773
 
8.3%
i5407627
 
7.3%
l5151305
 
7.0%
a3716039
 
5.0%
d3376345
 
4.6%
u3181654
 
4.3%
t3083295
 
4.2%
Other values (27)18644115
25.3%

faf_player_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1641
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246081.3662
Minimum144
Maximum434794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.0 MiB
2022-07-27T23:00:09.371553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum144
5-th percentile14947
Q1108922
median275960
Q3382548
95-th percentile431090
Maximum434794
Range434650
Interquartile range (IQR)273626

Descriptive statistics

Standard deviation146273.7171
Coefficient of variation (CV)0.5944120003
Kurtosis-1.378246707
Mean246081.3662
Median Absolute Deviation (MAD)125681
Skewness-0.283357954
Sum2.709587159 × 1012
Variance2.139600033 × 1010
MonotonicityNot monotonic
2022-07-27T23:00:09.427522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2642128382
 
1.2%
33632117469
 
1.1%
151257101112
 
0.9%
426415100733
 
0.9%
10580892342
 
0.8%
11655891373
 
0.8%
33508990704
 
0.8%
1984489874
 
0.8%
25694989088
 
0.8%
6492888863
 
0.8%
Other values (1631)10021000
91.0%
ValueCountFrequency (%)
1443581
 
< 0.1%
14512240
 
0.1%
25976215
0.7%
43418216
 
0.2%
43545049
0.4%
60611176
 
0.1%
64820497
 
0.2%
8996155
 
0.1%
9005427
 
< 0.1%
10651163
 
< 0.1%
ValueCountFrequency (%)
434794538
 
< 0.1%
434789405
 
< 0.1%
4347822798
< 0.1%
4347601
 
< 0.1%
434759832
 
< 0.1%
434758496
 
< 0.1%
4347531980
< 0.1%
4347443544
< 0.1%
4347361048
 
< 0.1%
434731336
 
< 0.1%

login
Categorical

HIGH CARDINALITY

Distinct1660
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
nemir
 
128382
silentNoob
 
117469
Solstice245
 
101112
fagina_humpalot
 
100733
lansraad01
 
92342
Other values (1655)
10470902 

Length

Max length16
Median length12
Mean length8.623896143
Min length2

Characters and Unicode

Total characters94957203
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowGlassy
2nd rowMorax
3rd rowtrandan
4th rowMorax
5th rowKeksyamba

Common Values

ValueCountFrequency (%)
nemir128382
 
1.2%
silentNoob117469
 
1.1%
Solstice245101112
 
0.9%
fagina_humpalot100733
 
0.9%
lansraad0192342
 
0.8%
coca91373
 
0.8%
Iruma-Kun90704
 
0.8%
Electrician89874
 
0.8%
b1adam89088
 
0.8%
Morax88863
 
0.8%
Other values (1650)10021000
91.0%

Length

2022-07-27T23:00:09.487013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nemir128382
 
1.2%
silentnoob117469
 
1.1%
solstice245101112
 
0.9%
fagina_humpalot100733
 
0.9%
lansraad0192342
 
0.8%
coca91373
 
0.8%
iruma-kun90704
 
0.8%
electrician89874
 
0.8%
b1adam89088
 
0.8%
morax88863
 
0.8%
Other values (1648)10021000
91.0%

Most occurring characters

ValueCountFrequency (%)
a8000104
 
8.4%
e7054644
 
7.4%
r6529821
 
6.9%
o6236728
 
6.6%
i5274867
 
5.6%
n5055565
 
5.3%
t3918019
 
4.1%
l3602500
 
3.8%
s3146205
 
3.3%
u2581629
 
2.7%
Other values (54)43557121
45.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71956765
75.8%
Uppercase Letter15385870
 
16.2%
Decimal Number6032350
 
6.4%
Connector Punctuation1308272
 
1.4%
Dash Punctuation273946
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8000104
 
11.1%
e7054644
 
9.8%
r6529821
 
9.1%
o6236728
 
8.7%
i5274867
 
7.3%
n5055565
 
7.0%
t3918019
 
5.4%
l3602500
 
5.0%
s3146205
 
4.4%
u2581629
 
3.6%
Other values (16)20556683
28.6%
Uppercase Letter
ValueCountFrequency (%)
S1301155
 
8.5%
N1097532
 
7.1%
A996613
 
6.5%
T959550
 
6.2%
R878787
 
5.7%
L785871
 
5.1%
K767418
 
5.0%
P758253
 
4.9%
B737636
 
4.8%
C723558
 
4.7%
Other values (16)6379497
41.5%
Decimal Number
ValueCountFrequency (%)
01219435
20.2%
11011279
16.8%
2959660
15.9%
3683473
11.3%
4436336
 
7.2%
6415674
 
6.9%
5407874
 
6.8%
7331704
 
5.5%
9297898
 
4.9%
8269017
 
4.5%
Connector Punctuation
ValueCountFrequency (%)
_1308272
100.0%
Dash Punctuation
ValueCountFrequency (%)
-273946
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin87342635
92.0%
Common7614568
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8000104
 
9.2%
e7054644
 
8.1%
r6529821
 
7.5%
o6236728
 
7.1%
i5274867
 
6.0%
n5055565
 
5.8%
t3918019
 
4.5%
l3602500
 
4.1%
s3146205
 
3.6%
u2581629
 
3.0%
Other values (42)35942553
41.2%
Common
ValueCountFrequency (%)
_1308272
17.2%
01219435
16.0%
11011279
13.3%
2959660
12.6%
3683473
9.0%
4436336
 
5.7%
6415674
 
5.5%
5407874
 
5.4%
7331704
 
4.4%
9297898
 
3.9%
Other values (2)542963
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII94957203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a8000104
 
8.4%
e7054644
 
7.4%
r6529821
 
6.9%
o6236728
 
6.6%
i5274867
 
5.6%
n5055565
 
5.3%
t3918019
 
4.1%
l3602500
 
3.8%
s3146205
 
3.3%
u2581629
 
2.7%
Other values (54)43557121
45.9%

rating
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct2338
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.9465493
Minimum-534
Maximum2263
Zeros167306
Zeros (%)1.5%
Negative472068
Negative (%)4.3%
Memory size84.0 MiB
2022-07-27T23:00:09.542707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-534
5-th percentile0
Q1399
median675
Q31064
95-th percentile1728
Maximum2263
Range2797
Interquartile range (IQR)665

Descriptive statistics

Standard deviation503.7306672
Coefficient of variation (CV)0.6771059932
Kurtosis-0.1242216603
Mean743.9465493
Median Absolute Deviation (MAD)313
Skewness0.4822597011
Sum8191550818
Variance253744.585
MonotonicityNot monotonic
2022-07-27T23:00:09.597311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0167306
 
1.5%
146650548
 
0.5%
73322222
 
0.2%
65021715
 
0.2%
67218879
 
0.2%
50017966
 
0.2%
49617781
 
0.2%
74517778
 
0.2%
47717412
 
0.2%
60417189
 
0.2%
Other values (2328)10642144
96.7%
ValueCountFrequency (%)
-534285
 
< 0.1%
-5291392
< 0.1%
-528173
 
< 0.1%
-526683
< 0.1%
-513611
< 0.1%
-510105
 
< 0.1%
-507513
 
< 0.1%
-503280
 
< 0.1%
-499317
 
< 0.1%
-497407
 
< 0.1%
ValueCountFrequency (%)
22631427
< 0.1%
2247947
 
< 0.1%
22342916
< 0.1%
2215869
 
< 0.1%
22131046
 
< 0.1%
22061630
< 0.1%
2203751
 
< 0.1%
22022745
< 0.1%
22011416
< 0.1%
21971130
 
< 0.1%

faction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
UEF
4288313 
Cybran
3289699 
Seraphim
1818564 
Aeon
1614364 

Length

Max length8
Median length6
Mean length4.86871248
Min length3

Characters and Unicode

Total characters53609101
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeraphim
2nd rowUEF
3rd rowSeraphim
4th rowAeon
5th rowUEF

Common Values

ValueCountFrequency (%)
UEF4288313
38.9%
Cybran3289699
29.9%
Seraphim1818564
16.5%
Aeon1614364
 
14.7%

Length

2022-07-27T23:00:09.650705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:00:09.699805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
uef4288313
38.9%
cybran3289699
29.9%
seraphim1818564
16.5%
aeon1614364
 
14.7%

Most occurring characters

ValueCountFrequency (%)
r5108263
9.5%
a5108263
9.5%
n4904063
 
9.1%
E4288313
 
8.0%
U4288313
 
8.0%
F4288313
 
8.0%
e3432928
 
6.4%
b3289699
 
6.1%
y3289699
 
6.1%
C3289699
 
6.1%
Other values (7)12321548
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34021535
63.5%
Uppercase Letter19587566
36.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r5108263
15.0%
a5108263
15.0%
n4904063
14.4%
e3432928
10.1%
b3289699
9.7%
y3289699
9.7%
p1818564
 
5.3%
h1818564
 
5.3%
i1818564
 
5.3%
m1818564
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
E4288313
21.9%
U4288313
21.9%
F4288313
21.9%
C3289699
16.8%
S1818564
9.3%
A1614364
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin53609101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r5108263
9.5%
a5108263
9.5%
n4904063
 
9.1%
E4288313
 
8.0%
U4288313
 
8.0%
F4288313
 
8.0%
e3432928
 
6.4%
b3289699
 
6.1%
y3289699
 
6.1%
C3289699
 
6.1%
Other values (7)12321548
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53609101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r5108263
9.5%
a5108263
9.5%
n4904063
 
9.1%
E4288313
 
8.0%
U4288313
 
8.0%
F4288313
 
8.0%
e3432928
 
6.4%
b3289699
 
6.1%
y3289699
 
6.1%
C3289699
 
6.1%
Other values (7)12321548
23.0%

map_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
Loki - FAF version
1630973 
Twin Rivers
1589679 
Eye of the Storm - FAF version
1502786 
Desert Arena - FAF version
1214030 
Ambush Pass
1156201 
Other values (23)
3917271 

Length

Max length32
Median length30
Mean length19.29911869
Min length6

Characters and Unicode

Total characters212501438
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegor VI Highlands
2nd rowWhite Fire - FAF version
3rd rowThe Bermuda Locket - FAF version
4th rowWhite Fire - FAF version
5th rowCrossfire Canal - FAF version

Common Values

ValueCountFrequency (%)
Loki - FAF version1630973
14.8%
Twin Rivers1589679
14.4%
Eye of the Storm - FAF version1502786
13.6%
Desert Arena - FAF version1214030
11.0%
Ambush Pass1156201
10.5%
Sentry Point953144
8.7%
Seraphim Glaciers - FAF version824821
7.5%
Syrtis Major - FAF version754944
6.9%
Broken_Vows615600
 
5.6%
Roanoke Abyss296179
 
2.7%
Other values (18)472583
 
4.3%

Length

2022-07-27T23:00:09.748615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6111653
14.8%
version6088467
14.8%
faf6088467
14.8%
loki1630973
 
4.0%
twin1589679
 
3.9%
rivers1589679
 
3.9%
the1568274
 
3.8%
of1509502
 
3.7%
eye1502786
 
3.6%
storm1502786
 
3.6%
Other values (47)12023288
29.2%

Most occurring characters

ValueCountFrequency (%)
30194614
14.2%
e18521057
 
8.7%
r16678951
 
7.8%
s15473354
 
7.3%
i14597678
 
6.9%
o14554590
 
6.8%
F12300544
 
5.8%
n12037789
 
5.7%
A8779831
 
4.1%
v7734628
 
3.6%
Other values (40)61628402
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter136491634
64.2%
Uppercase Letter38902722
 
18.3%
Space Separator30194614
 
14.2%
Dash Punctuation6120899
 
2.9%
Connector Punctuation663722
 
0.3%
Other Punctuation65513
 
< 0.1%
Decimal Number62334
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e18521057
13.6%
r16678951
12.2%
s15473354
11.3%
i14597678
10.7%
o14554590
10.7%
n12037789
8.8%
v7734628
 
5.7%
t7119571
 
5.2%
a5543133
 
4.1%
h3733237
 
2.7%
Other values (15)20497646
15.0%
Uppercase Letter
ValueCountFrequency (%)
F12300544
31.6%
A8779831
22.6%
S4042686
 
10.4%
P2167677
 
5.6%
R2039765
 
5.2%
L1696461
 
4.4%
T1680103
 
4.3%
E1502786
 
3.9%
D1217706
 
3.1%
G849757
 
2.2%
Other values (8)2625406
 
6.7%
Decimal Number
ValueCountFrequency (%)
429902
48.0%
823186
37.2%
39246
 
14.8%
Space Separator
ValueCountFrequency (%)
30194614
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6120899
100.0%
Connector Punctuation
ValueCountFrequency (%)
_663722
100.0%
Other Punctuation
ValueCountFrequency (%)
'65513
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin175394356
82.5%
Common37107082
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e18521057
 
10.6%
r16678951
 
9.5%
s15473354
 
8.8%
i14597678
 
8.3%
o14554590
 
8.3%
F12300544
 
7.0%
n12037789
 
6.9%
A8779831
 
5.0%
v7734628
 
4.4%
t7119571
 
4.1%
Other values (33)47596363
27.1%
Common
ValueCountFrequency (%)
30194614
81.4%
-6120899
 
16.5%
_663722
 
1.8%
'65513
 
0.2%
429902
 
0.1%
823186
 
0.1%
39246
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII212501438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30194614
14.2%
e18521057
 
8.7%
r16678951
 
7.8%
s15473354
 
7.3%
i14597678
 
6.9%
o14554590
 
6.8%
F12300544
 
5.8%
n12037789
 
5.7%
A8779831
 
4.1%
v7734628
 
3.6%
Other values (40)61628402
29.0%

map_thumbnail
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
https://content.faforever.com/maps/previews/large/loki_-_faf_version.v0003.png
1630973 
https://content.faforever.com/maps/previews/large/twin rivers.v0001.png
1589679 
https://content.faforever.com/maps/previews/large/eye_of_the_storm_-_faf_version.v0002.png
1502786 
https://content.faforever.com/maps/previews/large/desert_arena_-_faf_version.v0002.png
1214030 
https://content.faforever.com/maps/previews/large/scmp_038.png
1156201 
Other values (23)
3917271 

Length

Max length92
Median length90
Mean length77.08422206
Min length62

Characters and Unicode

Total characters848769744
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://content.faforever.com/maps/previews/large/regor_vi_highlands.v0006.png
2nd rowhttps://content.faforever.com/maps/previews/large/white_fire_-_faf_version.v0002.png
3rd rowhttps://content.faforever.com/maps/previews/large/the_bermuda_locket_-_faf_version.v0001.png
4th rowhttps://content.faforever.com/maps/previews/large/white_fire_-_faf_version.v0002.png
5th rowhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png

Common Values

ValueCountFrequency (%)
https://content.faforever.com/maps/previews/large/loki_-_faf_version.v0003.png1630973
14.8%
https://content.faforever.com/maps/previews/large/twin rivers.v0001.png1589679
14.4%
https://content.faforever.com/maps/previews/large/eye_of_the_storm_-_faf_version.v0002.png1502786
13.6%
https://content.faforever.com/maps/previews/large/desert_arena_-_faf_version.v0002.png1214030
11.0%
https://content.faforever.com/maps/previews/large/scmp_038.png1156201
10.5%
https://content.faforever.com/maps/previews/large/scmp_018.png953144
8.7%
https://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png824821
7.5%
https://content.faforever.com/maps/previews/large/syrtis_major_-_faf_version.v0002.png754944
6.9%
https://content.faforever.com/maps/previews/large/broken_vows.v0008.png615600
 
5.6%
https://content.faforever.com/maps/previews/large/scmp_020.png296179
 
2.7%
Other values (18)472583
 
4.3%

Length

2022-07-27T23:00:09.798588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://content.faforever.com/maps/previews/large/loki_-_faf_version.v0003.png1630973
12.8%
rivers.v0001.png1589679
12.4%
https://content.faforever.com/maps/previews/large/twin1589679
12.4%
https://content.faforever.com/maps/previews/large/eye_of_the_storm_-_faf_version.v0002.png1502786
11.8%
https://content.faforever.com/maps/previews/large/desert_arena_-_faf_version.v0002.png1214030
9.5%
https://content.faforever.com/maps/previews/large/scmp_038.png1156201
9.1%
https://content.faforever.com/maps/previews/large/scmp_018.png953144
7.5%
https://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png824821
6.5%
https://content.faforever.com/maps/previews/large/syrtis_major_-_faf_version.v0002.png754944
5.9%
https://content.faforever.com/maps/previews/large/broken_vows.v0008.png615600
 
4.8%
Other values (28)939647
7.4%

Most occurring characters

ValueCountFrequency (%)
e84699558
 
10.0%
/66065640
 
7.8%
r61457414
 
7.2%
t50902417
 
6.0%
s49942173
 
5.9%
p47366008
 
5.6%
o46009795
 
5.4%
a44372143
 
5.2%
n42739041
 
5.0%
.41500364
 
4.9%
Other values (30)313715191
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter651779566
76.8%
Other Punctuation118576944
 
14.0%
Decimal Number41421159
 
4.9%
Connector Punctuation29119858
 
3.4%
Dash Punctuation6111653
 
0.7%
Space Separator1760564
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e84699558
13.0%
r61457414
9.4%
t50902417
 
7.8%
s49942173
 
7.7%
p47366008
 
7.3%
o46009795
 
7.1%
a44372143
 
6.8%
n42739041
 
6.6%
v38964637
 
6.0%
f35849266
 
5.5%
Other values (14)149477114
22.9%
Decimal Number
ValueCountFrequency (%)
028235568
68.2%
23872631
 
9.3%
13540223
 
8.5%
32822431
 
6.8%
82748131
 
6.6%
685058
 
0.2%
757320
 
0.1%
429902
 
0.1%
926580
 
0.1%
53315
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/66065640
55.7%
.41500364
35.0%
:11010940
 
9.3%
Connector Punctuation
ValueCountFrequency (%)
_29119858
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6111653
100.0%
Space Separator
ValueCountFrequency (%)
1760564
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin651779566
76.8%
Common196990178
 
23.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e84699558
13.0%
r61457414
9.4%
t50902417
 
7.8%
s49942173
 
7.7%
p47366008
 
7.3%
o46009795
 
7.1%
a44372143
 
6.8%
n42739041
 
6.6%
v38964637
 
6.0%
f35849266
 
5.5%
Other values (14)149477114
22.9%
Common
ValueCountFrequency (%)
/66065640
33.5%
.41500364
21.1%
_29119858
14.8%
028235568
14.3%
:11010940
 
5.6%
-6111653
 
3.1%
23872631
 
2.0%
13540223
 
1.8%
32822431
 
1.4%
82748131
 
1.4%
Other values (6)1962739
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII848769744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e84699558
 
10.0%
/66065640
 
7.8%
r61457414
 
7.2%
t50902417
 
6.0%
s49942173
 
5.9%
p47366008
 
5.6%
o46009795
 
5.4%
a44372143
 
5.2%
n42739041
 
5.0%
.41500364
 
4.9%
Other values (30)313715191
37.0%

map_width
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
512
6433229 
256
3350573 
1024
1227138 

Length

Max length4
Median length3
Mean length3.111447161
Min length3

Characters and Unicode

Total characters34259958
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512
2nd row512
3rd row1024
4th row512
5th row1024

Common Values

ValueCountFrequency (%)
5126433229
58.4%
2563350573
30.4%
10241227138
 
11.1%

Length

2022-07-27T23:00:09.847023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:00:09.893427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5126433229
58.4%
2563350573
30.4%
10241227138
 
11.1%

Most occurring characters

ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34259958
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common34259958
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII34259958
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

map_height
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
512
6433229 
256
3350573 
1024
1227138 

Length

Max length4
Median length3
Mean length3.111447161
Min length3

Characters and Unicode

Total characters34259958
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512
2nd row512
3rd row1024
4th row512
5th row1024

Common Values

ValueCountFrequency (%)
5126433229
58.4%
2563350573
30.4%
10241227138
 
11.1%

Length

2022-07-27T23:00:09.935218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T23:00:09.982351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5126433229
58.4%
2563350573
30.4%
10241227138
 
11.1%

Most occurring characters

ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34259958
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common34259958
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII34259958
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211010940
32.1%
59783802
28.6%
17660367
22.4%
63350573
 
9.8%
01227138
 
3.6%
41227138
 
3.6%

Interactions

2022-07-27T22:58:33.769740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:30.137861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:41.790985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:52.500641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:02.101990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:12.904584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:22.810033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:35.448486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:31.943917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:43.362289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:53.954008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:03.436884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:14.224607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:24.481619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:37.087080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:33.640297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:45.053197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:55.290077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:04.762878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:15.556594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:26.110833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:38.418238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:35.051827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:46.356532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:56.487240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:06.031286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:16.862167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:27.434386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:39.770916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:36.472085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:47.670750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:57.759112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:07.360044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:18.147808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:28.798634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:41.519582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:38.322689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:49.387727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:59.303848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:10.230722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:19.473901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:30.427093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:43.124025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:40.098777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:57:51.032707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:00.765204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:11.578522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:20.811570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:58:32.102622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-27T23:00:10.025319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-27T23:00:10.107003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-27T23:00:10.190037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-27T23:00:10.390413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-27T23:00:10.465249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-27T22:58:50.551486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-27T22:59:13.215742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-27T22:59:49.118635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-27T22:59:57.862780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idstart_timeoffset_msplayertypeunits_numberblueprintposition_xposition_ycommand_namefaf_player_idloginratingfactionmap_namemap_thumbnailmap_widthmap_height
0161283162022-01-14 20:00:38 UTC10473001issue2NaNNaNNaNStop92766Glassy1577SeraphimRegor VI Highlandshttps://content.faforever.com/maps/previews/large/regor_vi_highlands.v0006.png512512
1161788912022-01-21 17:44:31 UTC9270001issue1NaNNaNNaNStop64928Morax1750UEFWhite Fire - FAF versionhttps://content.faforever.com/maps/previews/large/white_fire_-_faf_version.v0002.png512512
2162561132022-01-31 11:47:12 UTC14671001issue98NaN671.567932271.309540FormMove108922trandan1772SeraphimThe Bermuda Locket - FAF versionhttps://content.faforever.com/maps/previews/large/the_bermuda_locket_-_faf_version.v0001.png10241024
3161287002022-01-14 20:39:32 UTC4067001issue1NaNNaNNaNStop64928Morax1759AeonWhite Fire - FAF versionhttps://content.faforever.com/maps/previews/large/white_fire_-_faf_version.v0002.png512512
4160310452022-01-01 17:56:41 UTC6593001issue1NaNNaNNaNStop366264Keksyamba636UEFCrossfire Canal - FAF versionhttps://content.faforever.com/maps/previews/large/crossfire_canal_-_faf_version.v0002.png10241024
5161096852022-01-11 21:49:28 UTC1933001issue3NaN274.293945372.844299FormMove63950EcoNoob1965CybranThe Bermuda Locket - FAF versionhttps://content.faforever.com/maps/previews/large/the_bermuda_locket_-_faf_version.v0001.png10241024
6161691882022-01-20 04:17:32 UTC11881001issue1NaNNaNNaNCapture270014ikigami1939SeraphimRegor VI Highlandshttps://content.faforever.com/maps/previews/large/regor_vi_highlands.v0006.png512512
7160303232022-01-01 16:26:41 UTC7569001issue1NaNNaNNaNScript174608MAPTOC1466CybranVulcan's Reachhttps://content.faforever.com/maps/previews/large/vulcans reach.v0003.png512512
8160421492022-01-02 22:38:24 UTC15740001issue1NaNNaNNaNOverCharge33632silentNoob1745CybranRegor VI Highlandshttps://content.faforever.com/maps/previews/large/regor_vi_highlands.v0006.png512512
9160274062022-01-01 05:11:40 UTC12055000issue1NaNNaNNaNOverCharge356435Flanking_Octopus528SeraphimTAG_Craftious Maximushttps://content.faforever.com/maps/previews/large/tag_craftious maximus.v0001.png512512

Last rows

idstart_timeoffset_msplayertypeunits_numberblueprintposition_xposition_ycommand_namefaf_player_idloginratingfactionmap_namemap_thumbnailmap_widthmap_height
11010930160355162022-01-02 04:44:56 UTC10218001issue1NaN193.058014754.344666AggressiveMove362625Vaangard576UEFSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010931162473112022-01-30 11:35:19 UTC25873001issue57NaN233.420425685.052124AggressiveMove432736Raderblob527UEFSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010932161981702022-01-23 19:33:34 UTC7388000issue8NaN205.886200286.587585AggressiveMove219084Conorach1386AeonSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010933162539622022-01-31 01:08:07 UTC6314001issue1NaN673.692322207.052368AggressiveMove64928Morax1753AeonSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010934161586752022-01-18 16:05:33 UTC8060000issue1NaN402.206024235.428619AggressiveMove285415Hemfast838SeraphimSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010935162473112022-01-30 11:35:19 UTC13801001issue49NaN633.414185624.077637AggressiveMove432736Raderblob527UEFSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010936160618532022-01-05 16:50:13 UTC16088001issue1NaN815.276306622.230347AggressiveMove37910Bjorn1268UEFSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010937160871362022-01-08 20:41:18 UTC7384001issue17NaN241.172546766.024658AggressiveMove376123naSimka1246CybranSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010938160763912022-01-07 17:05:11 UTC7533000issue5NaN425.567688155.549957AggressiveMove25711Swkoll2149UEFSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024
11010939162495762022-01-30 16:23:12 UTC1593001factory_issue1NaN723.345886831.108093AggressiveMove297885AHTOHOB1317SeraphimSeraphim Glaciers - FAF versionhttps://content.faforever.com/maps/previews/large/seraphim_glaciers_-_faf_version.v0001.png10241024